This article deals with the stabilization of nonlinear cyber-physical systems (CPS) subject to actuator faults via the fault-tolerant control (FTC) algorithm. First, to tackle the time-varying delays taken into the system, we proposed a novel quadratic function negative determination lemma, which derives the sufficient conditions for the corresponding quadratic polynomials arising in the derivatives of Lyapunov-Krasovskii functional (LKF). For this purpose, we are parting the time-delay intervals into uniformly equal subintervals and the tangents intersection is carried out in the region of each partitioned intervals. Thereafter, from the cross-points of tangents in each subinterval and choosing a freely adjustable parameter within the delay bounds, we attained a novel quadratic function negative definiteness conditions which profits with high system performance. By means of Lyapunov stability theory (LST), the sufficient conditions are derived in the form of linear matrix inequalities that ensure the asymptotic stabilization of the addressed system. Finally, numerical simulations including the realm of autonomous ground vehicle (AGV) problem are performed, and the comparative analysis in the line of negative-definite (ND) lemmas is exhibited to showcase the efficacy of the proposed approach (PA).
This article proposes an accelerated learning control framework for point-to-point (P2P) tracking systems subject to stochastic noise, with a focus on reducing input energy. A novel stochastic accelerated method with a fixed penalty factor is established, resulting in substantial performance advancements for the overall iteration process. In this method, we introduce a two-loop structure. A historical term is designed and appropriately incorporated into the input update to improve the convergence process of the inner loop, and a Lagrange multiplier is updated in the outer loop to ensure the input sequence to converge to a limit that is closest to the initial input, achieving the effect of energy reduction. Additionally, practical implementation of the proposed framework is addressed by terminating the inner loop within a finite number of iterations according to a given accuracy. In this scenario, two types of Lagrange multiplier updating are conducted to handle the noise’s impact. Numerical simulations are provided to validate the theoretical results.
This article investigates optimization-driven learning techniques to address the critical challenge of balancing communication efficiency with convergence acceleration in distributed multiagent systems. While existing accelerated methods typically necessitate multiple internode communications per iteration, we propose two novel methods, heavy-ball exact fusion (HBEF) and Nesterov-accelerated exact fusion (NAEF), that maintain a single-communication operation while achieving enhanced convergence. By fusing momentum mechanisms with bias correction, the developed methods not only precisely preserve convergence guarantees but also demonstrate accelerated convergence compared to baseline exact diffusion and contemporary accelerated counterparts. Adistinctive dual acceleration method is further proposed through momentum parameter coordination. Rigorous convergence analysis reveals the momentum parameter’s critical role in acceleration behavior. Extensive numerical evaluations across representative machine learning tasks validate the proposed methods’ superiority in both transient convergence speed and steady-state accuracy. Notably, they achieve state-of-the-art performance at half or one-third the communication cost, effectively bridging the long-standing gap between communication efficiency and rapid convergence in distributed learning.
Given the high coupling of state variables in second-order chaotic systems, the projective synchronization control schemes for first- or fractional-order chaotic systems are not applicable in second-order chaotic systems. Also, the current control schemes on second-order chaotic systems are difficult to tradeoff between convergence and robustness. To address the above issues, this article develops a predefined-time robust neural dynamics controller (PTRNDC). First, a predefined-time nonsingular terminal sliding mode variable (PTNTSMV) is designed to control the coupling of errors in the projective synchronization of second-order chaotic systems, ensuring the nonsingularity and convergence. Hence, a predefined-time double-integral zeroing neural dynamics (ZNDs) design formula based on a time-base generator (TBG) is devised to ensure that the sliding mode variable attains the desired sliding mode surface swiftly and robustly. The theorems about the stability, convergence, and robustness of the projective synchronization under the PTRNDC are analyzed rigorously, and the comparative simulations further verify the effectiveness of the PTRNDC. In addition, the chaotic sequences generated by the projective synchronization between the second-order chaotic systems are successfully applied in the image encryption, making the original image possess excellent visual distortion.
This article develops a self-triggered prescribed-time (PT) smooth bipartite formation tracking control (BFTC) strategy for uncertain nonlinear multiagent systems (NMASs) operating over directed graphs. An adaptive backstepping framework is employed for formation control design. To enhance the applicability of distributed protocols, we investigate practical BFTC for multiagent systems (MASs) with followers that are subject to unknown nonlinear dynamics, external disturbances, and actuator faults within cooperative-competitive interaction topologies. Radial basis function neural networks (RBFNNs) are employed to approximate these uncertainties, leveraging their universal approximation capability and localized response characteristics. Importantly, in contrast to previous studies, this work achieves user-defined tracking performance suitable for practical NMAS implementations. The proposed bipartite formation tracking controller guarantees compliance with the user-specified settling time without reliance on initial conditions. Furthermore, considering the constraints imposed by limited communication bandwidth, a distributed dynamic self-triggered control (DSTC) mechanism is developed to enhance transmission efficiency. Unlike traditional strategies, the proposed DSTC dynamically adjusts triggering intervals based on bipartite formation tracking errors (BFTEs). This adaptability facilitates a real-time balance between communication load and system performance. Simulation results validate the efficacy of the proposed control strategy.
Electroencephalography (EEG) emotion recognition plays a key role in improving human-machine interactions. Advanced algorithms have been proposed for this task. However, two challenges remain, i.e., unclear decision boundary in the embedded space and noise in physiological signals from various devices. To this end, we develop a novel framework, namely, UACL-Net, for EEG emotion recognition. It is based on uncertainty-aware contrastive learning (UACL) and frequency-aware self-attention (FASA). Specifically, UACL uses a multivariate Gaussian distribution to construct the latent space for different emotions. It is able to highlight interclass differences, thereby improving the robustness of model decisions. In addition, FASA generates learnable weights by applying self-attention (SA) to the real and imaginary components in the frequency domain. This helps adaptively reduce noise and capture global dependencies in temporal sequences. Our model is trained and tested on four benchmark datasets, achieving up to 94.88%, 98.71%, 96.91%, and 99.29% accuracy on SEED, DEAP, DREAMER, and FACED, respectively. Experimental results demonstrate that it is effective and has advantages over peer state-of-the-art (SOTA) methods.
The problem of reinforcement learning (RL)-based fuzzy control for nonlinear systems with unknown dynamics via parallel composite policy iteration (PCPI) scheme is studied in this article. The main objective of this article is to solve the fuzzy algebraic Riccati equation (FARE), which is inherently complex and cannot be easily solved by traditional mathematical formulas. Policy iteration (PI) and value iteration (VI) algorithms proposed have been widely used to address this problem. However, these algorithms have the disadvantages of an initial stabilizing control policy, the persistent excitation (PE) condition, and huge amounts of data. To effectively alleviate these drawbacks, a novel PCPI algorithm is proposed in this article. Specifically, for each fuzzy subsystem, an adaptive parameter is designed to eliminate the requirement of an initial stabilizing control policy. In addition, an online model-free PCPI algorithm is proposed for the situation where the dynamic information of the fuzzy system is difficult to obtain. By substituting the stored historical data with online data, the PE condition is relaxed to the initial excitation (IE) condition. Concurrently, the corresponding algorithm can be executed independently and concurrently under each fuzzy rule, thereby fully exploiting the available computational resources. Finally, the effectiveness of the algorithms set forth in this article is verified through a single-link robot arm and quarter-car active suspension (QCAS) experiment.
Test-time few-shot object detection (FSOD) represents an innovative approach for identifying novel categories using a limited number of support examples, obviating the need for model fine-tuning. Despite advancements, existing FSOD methods, including our prior work, continue to grapple with challenges posed by domain/category shift and limited data availability. Building upon our previous research on test-time FSOD, this article proposes a novel dynamic prototype fusion network (PFN) to overcome these limitations. To mitigate the impact of the distribution shift, a dynamic prototype refinement method is introduced that updates prototypes from supporting images in an adaptive manner. Further, limited samples are mitigated through exhaustive exploitation of information within support images. Specifically, we design a dual-level multiscale information integration approach that effectively fuses information across different network layers and image scales, enhancing the model’s discriminating capabilities. Additionally, a mask-based preprocessing technique harnesses segmentation labels on support samples, effectively suppressing the adverse impact of background noise on model accuracy. Notably, to align with the constraints of test-time scenarios, model parameters remain fixed during the configuration step, with only prototypes being updated each time users input novel supporting samples. As a result, our method achieves superior performance over existing state-of-the-art FSOD methods on multiple benchmarks, demonstrating remarkable potential in the realm of FSOD. The code is available at https://github.com/CatfishW/TIDEV2
This article investigates the security control issue of delayed coupled fuzzy inertial neural networks (FINNs) under deception attacks. Aiming to alleviate the influence of deception attacks, a fuzzy sampling data security controller is designed. A theoretical structure is formulated to analyze the behavior of the closed-loop system under deceptive interference. On this basis, by constructing a suitable set of Lyapunov functionals (LKFs) and employing inequality techniques, criteria guaranteeing exponential synchronization are established using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is demonstrated via numerical simulations and encryption and decryption analysis. Results show that, affected by deception attacks, the coupling FINNs can achieve exponential synchronization through our developed security control approach.
Constructing memristive neural networks (MNNs) with multiscroll chaotic attractors helps advance both theoretical and applied research on neural networks. However, the existing models mainly utilize complex memristor models with polynomial functions, nested composite functions, and so on, to generate multiscroll chaotic attractors, which leads to increased model complexity and difficulties in on-demand adjustment. Hence, this article proposes a reconfigurable multiscroll MNN (RMMNN) that can yield different types of multiscroll chaotic attractors merely by altering the memristive parameters without modifying its model. Through numerical methods, the complex dynamics of the RMMNN in different cases are analyzed, such as parameter-controlled multiscroll chaotic attractors, adjustable multistability, and parameter-induced transitions of multistability. In addition, the reliability of the numerical analysis is verified via the hardware circuits. Moreover, to address the issues of image security and low quality in telemedicine, a bidirectional rotation medical image encryption scheme (BRMIES) is developed based on the good pseudorandom chaotic sequences generated by RMMNN. Performance analysis demonstrates that BRMIES can effectively protect medical image and robustly handle various potential adverse interferences within telemedicine process.
Soft sensors are essential for advanced monitoring and control to prevent undesirable operations and improve product quality. However, nonlinear, autocorrelated, and cross-correlated behaviors in industrial data demand concurrent modeling of the dynamics and nonlinearities. Deep learning-based soft sensors, such as recurrent neural network (RNN) and long short-term memory (LSTM) networks, often incorporate complex structures and numerous parameters, which can lead to an overly complex model. In practical applications where training data samples are limited, a lightweight neural network with strong generalization capability is preferred. With a simple structure of feed-forward layers of 1-D convolutional neural networks (CNNs) (1-D-CNN) for time-series data modeling, this article proposes a novel lightweight dynamic CNN (LDCNN) for soft sensors. Positional embedding (PE) and simplified temporal attention mechanisms are integrated for improved dynamic modeling, while dilated convolutions and layer normalization (LN) are incorporated to significantly reduce the depth and width of the network and avoid over-parametrization. Experimental results on a real industrial case indicate that a lightweight model outperforms the traditional methods with limited training samples.
This article investigates the problem of secure state estimation for multitarget tracking systems based on Kalman consensus filtering. In the existing distributed Kalman filters, the filter gain and consensus structure rely on the independence of tracked targets, which cannot maintain the estimation performance when encountering coupled measurements across multiple targets. Moreover, the existing researches mainly focus on the security in single-channel systems, whereas such efforts fail to consider potential attacks in multichannel scenarios. In this case, by establishing a target-dependent augmented system and a link-unreliable composite directed graph, the coupling features and multichannel attacks are depicted. Then, a modified Kalman consensus filter is proposed by specifically designing consensus structure and gain terms to account for the impacts of coupled measurements and attacks. Furthermore, by scaling the Lyapunov function through the Riccati difference equation and matrix inequalities, sufficient conditions are established to ensure the boundedness of estimation errors. Numerical simulations are conducted to demonstrate the effectiveness of the filter.
Asymptotic state regulation of fully actuated systems (FASs) with time-varying unknown parameters, perturbed input matrices, and nonlinear uncertainties is considered. Compared to the closely related results on FASs with time-varying parameters, the requirement that the time-varying parameters are differentiable and the assumptions imposed on their derivatives in those works are no longer needed in this article, which means that many types of time-varying parameters that were difficult to handle by previous methods, such as those that are continuous and bounded but not differentiable, can now be handled. Furthermore, inspired by the congelation of variables method, a novel robust adaptive method is proposed, which achieves the global asymptotic convergence of the state variables instead of the global boundedness obtained in previous methods, and guarantees the global boundedness of the estimation. In the developed controller, the adaptive part compensates for time-varying parameters, and the robust part overcomes the effects of incomplete compensation and other remaining uncertainties. Moreover, a parallel extension of the developed method to the disturbed case and a discussion on parameter selection are given. Finally, the proposed method is successfully applied to the control of resonant circuit systems and Norrbin ship steering systems.
This article addresses the challenges posed by multimode denial of service (DoS) and deception attacks in observer-based sliding mode control (SMC) for fuzzy nonlinear systems. A novel multimode DoS attack model is introduced, incorporating time-varying sojourn probabilities to provide a more accurate and computationally efficient representation of the stochastic nature of these attacks. This model overcomes the limitations of traditional Markov-based models by capturing dynamic attack behaviors. Deception attacks are modeled as unbounded nonlinear functions, and adaptive neural networks (NNs) are employed to approximate their complex behaviors, significantly reducing their detrimental impact on system stability. A fuzzy sliding surface is designed based on the switching rule for sojourn probabilities, and an observer-based SMC law is proposed to ensure the mean square estimation upper bound of the fuzzy nonlinear systems, guaranteeing stability despite the presence of cyberattacks. Finally, the validity and superiority of the proposed control strategy are demonstrated through a tunnel diode circuit model.
Multiagent reinforcement learning (MARL) has shown strong performance in cooperative tasks. However, most existing approaches are designed for single-task scenarios and struggle to adapt to complex and dynamic environments. Multitask MARL methods aim to improve adaptability by sharing policies across tasks, but they often suffer from negative transfer due to conflicting task-specific knowledge. To address this, we propose dual-policy fusion for multitask MARL (DPF-MTMARL), which explicitly integrates a shared policy for leveraging common knowledge and task-specific policies for capturing task-specific information. Specifically, in DPF-MTMARL, we propose a learning method to efficiently train the task-specific policies and provide corresponding theoretical analysis. Additionally, we derive the theoretical conditions for decentralizing the joint policy and enforce these conditions through a regularization term during training. Extensive experiments demonstrate that DPF-MTMARL significantly outperforms state-of-the-art baselines in both homogeneous and heterogeneous task sets, effectively mitigating negative transfer and enabling robust multitask learning.
This article proposes a finite-time prescribed performance control (FTPPC) method for interconnected nonlinear systems with input saturation. By adding nonnegative auxiliary signals to the setting time of finite-time prescribed performance functions (FTPPFs), we present saturation-tolerant FTPPFs, which are easy to observe the convergence time. Compared with traditional FTPPFs, saturation-tolerant FTPPFs are able to expand from or restore to the expect constraint boundaries based on the input saturation error as well as whether the system enters the collision avoidance regions. Thus, the potential conflicts between input saturation and FTPPFs are resolved. Combined with saturation-tolerant FTPPFs, a low-complexity control algorithm is proposed, which omits the need for a function to estimate the unknown terms and reduces the computation. With the designed control scheme, the boundedness of all closed-loop signals is strictly proved when the feasibility condition is satisfied. Ultimately, simulations are presented to show the capability of the designed controller.
This article develops an event-triggered safe critic learning control (ESCLC) algorithm for nonlinear systems subject to asymmetric state constraints by integrating a safe critic learning control (SCLC) framework with an event-triggering mechanism. The SCLC algorithm innovatively incorporates control barrier functions into the safe value function design, addressing the challenge of deriving optimal control policies that guarantee system safety. Convergence of the SCLC algorithm is rigorously established within the value iteration framework, along with a criterion for assessing the admissibility of control policies. To enhance the application value of the algorithm in resource-constrained scenarios, an event-triggering mechanism is incorporated into the SCLC framework, yielding the ESCLC algorithm. The resulting closed-loop system under the ESCLC algorithm is proved to be asymptotically stable, and an upper bound on the actual value function is derived to ensure bounded performance degradation. In addition, a policy improvement method based on particle swarm optimization is designed that eliminates dependence on the system control matrix. Finally, the effectiveness of the ESCLC algorithm is verified through simulation experiments on a torsion pendulum system and a ball-and-beam system.
This article addresses the predefined-time optimal motion control problem of an autonomous surface vehicle (ASV) with disturbances under dynamic self-triggered frameworks via reinforcement learning (RL). Initially, to eliminate the influence of disturbance on the ASV, a predefined-time second-order integral sliding mode control (SOISM) strategy is formulated by establishing a novel integral sliding mode (ISM) function and a terminal sliding mode function. Subsequently, a predefined-time approximate optimal motion (AOM) control strategy is further developed to ensure the ASV maintains a stable state. Furthermore, a single critic network is used to obtain an approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. The above two strategies are established under the dynamic self-triggered framework, which relies on the current information to predict the next updating time, effectively reducing the computational and communication burden while avoiding the continuous monitoring of the ASV state. In the theoretical analysis, the main challenges lie in the design of Lyapunov functions and triggered conditions to ensure the stability of the sliding mode dynamics and the disturbed ASV. By applying the Lyapunov stability principle and designing two novel Lyapunov functions and triggered conditions that both contain dynamic variables, we demonstrate that the developed control strategies can ensure the stability within the specified time frame. Ultimately, simulation results verify the efficacy of the proposed motion control approach.
This work aims at developing a novel direct design and analysis method of learning control protocol toward consensus performance of multiagent systems (MASs) without using any model. A nonlinear autoregressive moving average (NARMA) function is designed at first to formulate the inherent consensus dynamics with respect to the consensus error and the control protocols. Then, a consensus performance-related iterative linear data model (CPiLDM) is constructed for equivalently reformulating the NARMA consensus system’s iterative dynamics in a data-driven framework. The CPiLDM does not rely on a model no matter through first-principle modeling or system identification methods. Next, a direct distributed iterative learning control (DirDILC) method is developed through an optimization technique subject to the CPiLDM. The convergence is proved directly for the virtual NARMA consensus system, without relying on the dynamics of the agent itself, and thus simplifies the analysis consequently. Since the presented DirDILC is purely data-driven without relying on an explicit model, it constitutes a significant step forward from the existing consensus control theory.
Analyzing conflicts between multiple objects within fuzzy information systems (ISs) and providing effective conflict resolution are challenging tasks due to the complexity and uncertainty of the real world. This article proposes a three-way conflict analysis and resolution model for intuitionistic fuzzy ISs (IFISs) based on three-way concept analysis (3WCA). First, the straight and vertical distances between intuitionistic fuzzy values (IFVs) are defined to obtain an intuitionistic fuzzy probability-credibility distribution. Second, intuitionistic fuzzy optimistic and pessimistic formal contexts are proposed based on two novel binary relations of intuitionistic fuzzy information. Afterward, two intuitionistic fuzzy concept lattices are obtained to analyze the marginal conflict degrees among multiple objects via consistency, inconsistency, and uncertainty attribute sets. By introducing three pairs of thresholds, the trisections of object pairs, objects, and attributes are derived through the three-way decision (3WD) process. Specifically, the maximal alliance unit, minimal conflict unit, and feasible strategy set are identified according to the three-way conflict analysis results. Subsequently, the consistency measure extracted from the intuitionistic fuzzy distance matrix is utilized to rank all feasible strategies. Finally, the experimental results are conducted to verify the effectiveness, superiority, and feasibility of the proposed model.
